MEDiC: Mitigating EEG Data Scarcity Via Class-Conditioned Diffusion Model

Learning with a small-scale Electroencephalography (EEG) dataset is a non-trivial task. On the other hand, collecting a large-scale EEG dataset is equally challenging due to subject availability and procedure sophistication constraints. Data augmentation offers a potential solution to address the shortage of data; however, traditional augmentation techniques are inefficient for EEG data. In this paper, we propose MEDiC, a class-conditioned Denoising Diffusion Probabilistic Model (DDPM) based approach to generate synthetic EEG embeddings. We perform experiments on a publicly accessible dataset. Empirical findings indicate that MEDiC efficiently generates synthetic EEG embeddings, which can serve as effective proxies to original EEG data. The code & pre-trained model will be made publicly available after the paper's acceptance.